Most medium to large businesses operate customer contact centers to provide services to customers. Modern contact centers often support various channels of communication for customer interactions, including telephony, e-mails, web-page forms and instant messaging. Telephony includes automatic call handling as well as call handling by human agents at the call centers. The majority of current contact center interactions comprise telephone conversations between contact center agents and customers.
Customer and agent conversations are a valuable source of insights into the contact center operations as well as the company's overall business. For instance, in depth analysis of call center conversations can enable measurement of customer satisfaction, identification of additional sales opportunities, identification of recurrent issues, and monitoring of contact center performance. However, when faced with a huge volume of calls, companies are not able to fully utilize the available information.
Existing techniques for call analysis are generally limited to applying natural language processing (NLP) techniques to automatic call routing through an interactive voice response system and call topic classification based on a predefined domain taxonomy. However, it is desirable to automatically provide ongoing learning from past interactions not only for call routing, but also for automated call handling, expedited call resolution, satisfaction monitoring, performance monitoring and information gathering.
Most contact center calls follow a well defined script or guideline. For example, a customer call to a contact center usually starts with a greeting and then proceeds into problem description, research on the problem, solution presentation, and a closing segment. Call segmentation and analysis of the call segments can improve search and retrieval functions and provide more detailed call statistics enabling interesting applications for business intelligence. For instance, contact centers today can determine the overall elapsed call handling time for an agent through the telephony system but cannot determine how the agent had spent the elapsed time (e.g., how long the agent takes to understand the customer's question; how long it takes the agent to identify a solution, or how long it takes for the agent to explain the solution to the customer). The time statistics for different call segments would be valuable to help contact center managers identify areas for improvement. For instance, the management could identify call topics which typically take a very long resolution time and provide additional agent training on the identified call topics.
Currently, call center consulting companies identify call segments manually. Consultants analyze calls by listening to live or recorded calls and by measuring the time statistics on a few important call sections manually. Since the manual approach is expensive and slow, contact centers can study only a very small number of calls.
It is, accordingly, an objective of the invention to provide automatic call segmentation for analysis of contact center calls.